{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "import tensorflow as tf\r\n",
    "import numpy as np\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "tf.compat.v1.disable_eager_execution()\r\n",
    "x_data = np.linspace(-0.5,0.5,200)[:,np.newaxis]\r\n",
    "\r\n",
    "noise = np.random.normal(0,0.02,x_data.shape)\r\n",
    "\r\n",
    "y_data = np.square(x_data)+noise\r\n",
    "#定义两个placeholder\r\n",
    "x = tf.compat.v1.placeholder(tf.float32,[None,1])\r\n",
    "y = tf.compat.v1.placeholder(tf.float32,[None,1])\r\n",
    "#定义神经网络中间层\r\n",
    "Weights_L1 = tf.compat.v1.Variable(tf.compat.v1.random_normal([1,10]))\r\n",
    "biases_L1 = tf.Variable(tf.zeros([1,10]))\r\n",
    "Wx_plus_L1 = tf.matmul(x,Weights_L1)+biases_L1\r\n",
    "L1 = tf.nn.tanh(Wx_plus_L1)\r\n",
    "#定义神经网络输出层\r\n",
    "Weights_L2 = tf.compat.v1.Variable(tf.compat.v1.random_normal([10,1]))\r\n",
    "biases_L2 = tf.Variable(tf.zeros([1,1]))\r\n",
    "Wx_plus_L2 = tf.matmul(L1,Weights_L2)+biases_L2\r\n",
    "\r\n",
    "prediction = tf.nn.tanh(Wx_plus_L2)\r\n",
    "#二次代阶函数\r\n",
    "loss = tf.reduce_mean(tf.square(y-prediction))\r\n",
    "#使用梯度下降法训练\r\n",
    "train_step = tf.compat.v1.train.GradientDescentOptimizer(0.1).minimize(loss)\r\n",
    "\r\n",
    "with tf.compat.v1.Session() as sess:\r\n",
    "    sess.run(tf.compat.v1.global_variables_initializer())\r\n",
    "    for _ in range(2000):\r\n",
    "        sess.run(train_step,feed_dict={x:x_data,y:y_data})\r\n",
    "    #获得预测值\r\n",
    "    prediction_value = sess.run(prediction,feed_dict={x:x_data})\r\n",
    "    plt.figure()\r\n",
    "    plt.scatter(x_data,y_data)\r\n",
    "    plt.plot(x_data,prediction_value,'r-',lw=5)\r\n",
    "    plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.8.8",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.8 64-bit ('base': conda)"
  },
  "interpreter": {
   "hash": "73e03da126b73bfff3642ec5261d56fa25c444ea595de51041687efaa60dda41"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}